Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add DataSift-Ty-Personal/SiftStack --skill sift-market-researchgit clone --depth 1 https://github.com/DataSift-Ty-Personal/SiftStackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datasift-ty-personal/siftstack/sift-market-research)<a href="https://agentmods.dev/skills/datasift-ty-personal/siftstack/sift-market-research"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/sift-market-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datasift-ty-personal/siftstack/sift-market-research"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/sift-market-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 20 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.09202 |
| Opus 5 | $0.00042 | $0.04601 |
| Sonnet 5 | $0.00017 | $0.01840 |
| Haiku 4.5 | $0.00008 | $0.00920 |
Grade A, and why
sift-market-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 781 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sift Market Research
Automate market research using Sift's Market Finder combined with public data sources to produce comprehensive, actionable market analysis for real estate investors.
Mandatory Output Requirements
All data outputs MUST include a properly formatted Excel spreadsheet (.xlsx). Never use plain text files (.txt) or poorly formatted data dumps.
| Output Type | Required Format | Template Reference |
|---|---|---|
| Quick Research | Excel (.xlsx) | templates/MarketFinderResearchExample.xlsx |
| Comprehensive Analysis | Excel (.xlsx) + Markdown Report | Use template structure |
| Data Exports | Excel (.xlsx) | Multi-sheet workbook |
Output Rules:
- All tabular data MUST be in Excel spreadsheet format
- Use the template at
templates/MarketFinderResearchExample.xlsxas the structural guide - Include proper column headers, formatting, and multiple worksheets as needed
- Pairing a Markdown report with the Excel spreadsheet is encouraged for comprehensive analysis
- NEVER output raw text files or unformatted data dumps as standalone deliverables
Acceptable Output Combinations:
- Excel spreadsheet only (for quick research)
- Excel spreadsheet + Markdown report (for comprehensive analysis)
- Excel spreadsheet + PDF summary (when requested)
NOT Acceptable:
- Text files (.txt) containing data tables
- Markdown-only outputs with no accompanying spreadsheet
- Unformatted data dumps
Credentials
Use your DataSift account credentials (email and password from app.reisift.io). Never hardcode credentials in skill files, prompts, or shared documents.
Login URL: https://app.reisift.io/
Execution Mode Detection
Before starting, detect the execution environment to determine whether automation is available:
Check 1: Does scripts/extract_market_finder.py exist in this skill's directory?
Check 2: Is Playwright available? Run: python -c "from playwright.sync_api import sync_playwright; print('OK')"
Check 3: Are credentials set? Check for DATASIFT_EMAIL and DATASIFT_PASSWORD in .env or environment variables.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 781 lines · 83 tokens per session scan A 00740675e0e0
sift-market-research is a skill published in the GitHub repository DataSift-Ty-Personal/SiftStack (21 stars, last pushed 5d ago), licensed MIT. It adds 83 tokens to every session and 9,202 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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